import pandas as pd
import datetime as datetime
import plotly.express as pexpr
import matplotlib.pyplot as plt
from plotly.offline import iplot
location_info = pd.read_csv('csv/Pedestrian_Counting_System_-_Monthly__counts_per_hour_.csv')
location_sensor_info = pd.read_csv('csv/Pedestrian_Counting_System_-_Sensor_Locations.csv')
location_info['Date_Time'] = pd.to_datetime(location_info['Date_Time'])
location_sensor_info[location_sensor_info['status']=='I']
| sensor_id | sensor_description | sensor_name | installation_date | status | note | direction_1 | direction_2 | latitude | longitude | location | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 71 | 55 | Elizabeth St-La Trobe St (East) | Eli380_T | 2018/07/19 | I | NaN | South | North | -37.809876 | 144.961349 | (-37.80987625, 144.96134928) |
| 72 | 52 | Elizabeth St-Lonsdale St (South) | Eli263_T | 2017/07/31 | I | NaN | East | West | -37.812508 | 144.961946 | (-37.81250841, 144.96194607) |
location_sensor_info[location_sensor_info['status']=='R']
| sensor_id | sensor_description | sensor_name | installation_date | status | note | direction_1 | direction_2 | latitude | longitude | location | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 32 | City Square | CSq_T | 2013/12/20 | R | Device has been removed (24/01/2017) | NaN | NaN | -37.815724 | 144.966863 | (-37.81572426, 144.96686315) |
| 23 | 14 | Sandridge Bridge | SanBri_T | 2009/03/24 | R | Sensor relocated to sensor ID 25 on 2/10/2019 | South | North | -37.820099 | 144.962925 | (-37.82009926, 144.96292494) |
| 27 | 25 | Melbourne Convention Exhibition Centre | MCEC_T | 2013/08/28 | R | NaN | East | West | -37.824005 | 144.956050 | (-37.8240046, 144.95605022) |
| 29 | 13 | Flagstaff Station | Wil277_T | 2009/03/24 | R | NaN | NaN | NaN | -37.812384 | 144.956532 | (-37.81238363, 144.95653249) |
| 31 | 22 | Flinders St-Elizabeth St (East) | Eli274_T | 2013/08/12 | R | NaN | South | North | -37.817851 | 144.965074 | (-37.81785129, 144.9650742) |
| 41 | 33 | Flinders St-Spring St (West) | Spr13_T | 2014/04/23 | R | NaN | South | North | -37.814819 | 144.974547 | (-37.81481936, 144.97454651) |
| 49 | 16 | Australia on Collins | Col270_T | 2009/03/30 | R | Device moved to location ID 53 (22/09/2015) | NaN | NaN | -37.815721 | 144.965216 | (-37.81572107, 144.96521642) |
| 51 | 38 | Flinders St-Swanston St (West) | Swa11_T | 2014/12/05 | R | Device has been removed (17/02/2017) | NaN | NaN | -37.817221 | 144.967156 | (-37.81722121, 144.9671563) |
| 52 | 34 | Flinders St-Spark La | Fli32_T | 2014/06/08 | R | NaN | East | West | -37.815367 | 144.974156 | (-37.81536669, 144.97415645) |
| 60 | 60 | Flinders La - Swanston St (West) Temporary | Swa31T_T | 2019/03/08 | R | Temporary for the duration of the metro tunnel... | South | North | -37.816669 | 144.966901 | (-37.81666873, 144.96690138) |
| 65 | 15 | State Library | QV_T | 2009/03/25 | R | NaN | South | North | -37.810631 | 144.964477 | (-37.81063062, 144.96447729) |
merge_pandas = location_info.merge(location_sensor_info, how='inner', left_on='Sensor_ID', right_on='sensor_id')
active_sensor_df = merge_pandas[merge_pandas['status']=='A'].reset_index(drop=True)
daily_count = active_sensor_df.groupby(['Day','Sensor_ID','Sensor_Name'],as_index=False).agg({'Hourly_Counts': 'sum'}).sort_values(by='Day',ascending=False)
days = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
for day in days:
print(daily_count[daily_count.Day==day].sort_values(by='Hourly_Counts',ascending=False).head(10))
print('\n')
Day Sensor_ID Sensor_Name Hourly_Counts
82 Monday 4 Town Hall (West) 19272251
84 Monday 6 Flinders Street Station Underpass 17752947
81 Monday 3 Melbourne Central 15577022
80 Monday 2 Bourke Street Mall (South) 14689966
83 Monday 5 Princes Bridge 14521729
79 Monday 1 Bourke Street Mall (North) 14218144
97 Monday 24 Spencer St-Collins St (North) 11777417
87 Monday 9 Southern Cross Station 9310691
104 Monday 35 Southbank 8602186
100 Monday 28 The Arts Centre 8334224
Day Sensor_ID Sensor_Name Hourly_Counts
398 Tuesday 4 Town Hall (West) 19457168
400 Tuesday 6 Flinders Street Station Underpass 18461430
397 Tuesday 3 Melbourne Central 16073224
396 Tuesday 2 Bourke Street Mall (South) 14429616
399 Tuesday 5 Princes Bridge 14396109
395 Tuesday 1 Bourke Street Mall (North) 14273069
413 Tuesday 24 Spencer St-Collins St (North) 12543187
403 Tuesday 9 Southern Cross Station 10230535
420 Tuesday 35 Southbank 8956773
416 Tuesday 28 The Arts Centre 8456510
Day Sensor_ID Sensor_Name Hourly_Counts
477 Wednesday 4 Town Hall (West) 20360823
479 Wednesday 6 Flinders Street Station Underpass 19002309
476 Wednesday 3 Melbourne Central 16657083
478 Wednesday 5 Princes Bridge 15592856
475 Wednesday 2 Bourke Street Mall (South) 15343311
474 Wednesday 1 Bourke Street Mall (North) 15043324
492 Wednesday 24 Spencer St-Collins St (North) 12922557
482 Wednesday 9 Southern Cross Station 10305852
495 Wednesday 28 The Arts Centre 9184199
499 Wednesday 35 Southbank 9181249
Day Sensor_ID Sensor_Name Hourly_Counts
319 Thursday 4 Town Hall (West) 21187600
321 Thursday 6 Flinders Street Station Underpass 19413550
318 Thursday 3 Melbourne Central 17019854
317 Thursday 2 Bourke Street Mall (South) 16322770
316 Thursday 1 Bourke Street Mall (North) 15965870
320 Thursday 5 Princes Bridge 15704870
334 Thursday 24 Spencer St-Collins St (North) 12928593
324 Thursday 9 Southern Cross Station 10424560
341 Thursday 35 Southbank 9364584
337 Thursday 28 The Arts Centre 9160781
Day Sensor_ID Sensor_Name Hourly_Counts
3 Friday 4 Town Hall (West) 24393876
5 Friday 6 Flinders Street Station Underpass 21211433
2 Friday 3 Melbourne Central 19853833
1 Friday 2 Bourke Street Mall (South) 18800045
4 Friday 5 Princes Bridge 18367835
0 Friday 1 Bourke Street Mall (North) 18348011
18 Friday 24 Spencer St-Collins St (North) 12951080
25 Friday 35 Southbank 10917020
21 Friday 28 The Arts Centre 10007238
8 Friday 9 Southern Cross Station 9761409
Day Sensor_ID Sensor_Name Hourly_Counts
161 Saturday 4 Town Hall (West) 22499236
160 Saturday 3 Melbourne Central 19012837
162 Saturday 5 Princes Bridge 18233579
159 Saturday 2 Bourke Street Mall (South) 17003142
158 Saturday 1 Bourke Street Mall (North) 16748863
163 Saturday 6 Flinders Street Station Underpass 15314200
183 Saturday 35 Southbank 10878637
179 Saturday 28 The Arts Centre 9753895
188 Saturday 41 Flinders La-Swanston St (West) 7017338
176 Saturday 24 Spencer St-Collins St (North) 6912910
Day Sensor_ID Sensor_Name Hourly_Counts
240 Sunday 4 Town Hall (West) 18172940
239 Sunday 3 Melbourne Central 15544124
241 Sunday 5 Princes Bridge 15480437
237 Sunday 1 Bourke Street Mall (North) 13626411
238 Sunday 2 Bourke Street Mall (South) 13405435
242 Sunday 6 Flinders Street Station Underpass 12931378
262 Sunday 35 Southbank 9037869
258 Sunday 28 The Arts Centre 8853109
253 Sunday 21 Bourke St-Russell St (West) 5789151
267 Sunday 41 Flinders La-Swanston St (West) 5716816
fig_sc1={}
days = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
for i in days:
df_day = daily_count[daily_count.Day==i].sort_values(by='Hourly_Counts',ascending=False).head(10)
fig_sc1[i]= pexpr.line(df_day, x='Sensor_Name', y= "Hourly_Counts", title=f"Daily {i} Pedestrian count")
iplot(fig_sc1[i])
months = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']
monthly_count= active_sensor_df.groupby(['Month','Sensor_ID','Sensor_Name'],as_index=False).agg({'Hourly_Counts': 'sum'}).sort_values(by='Month',ascending=False)
for month in months:
print(monthly_count[monthly_count.Month==month].sort_values(by='Hourly_Counts',ascending=False).head(10))
print('\n')
Month Sensor_ID Sensor_Name Hourly_Counts
294 January 4 Town Hall (West) 11967639
295 January 5 Princes Bridge 10796073
296 January 6 Flinders Street Station Underpass 10279628
292 January 2 Bourke Street Mall (South) 9106886
293 January 3 Melbourne Central 8433264
291 January 1 Bourke Street Mall (North) 8136291
316 January 35 Southbank 6279974
312 January 28 The Arts Centre 6214671
309 January 24 Spencer St-Collins St (North) 6096117
297 January 7 Birrarung Marr 4494074
Month Sensor_ID Sensor_Name Hourly_Counts
217 February 4 Town Hall (West) 11156945
219 February 6 Flinders Street Station Underpass 10292516
218 February 5 Princes Bridge 9493960
216 February 3 Melbourne Central 9004266
214 February 1 Bourke Street Mall (North) 8335328
215 February 2 Bourke Street Mall (South) 8134134
232 February 24 Spencer St-Collins St (North) 6591467
235 February 28 The Arts Centre 5657723
239 February 35 Southbank 5462142
222 February 9 Southern Cross Station 4481588
Month Sensor_ID Sensor_Name Hourly_Counts
512 March 4 Town Hall (West) 12655259
511 March 3 Melbourne Central 11737486
514 March 6 Flinders Street Station Underpass 11419121
513 March 5 Princes Bridge 11001403
509 March 1 Bourke Street Mall (North) 9643824
510 March 2 Bourke Street Mall (South) 9017586
527 March 24 Spencer St-Collins St (North) 7149300
534 March 35 Southbank 5930487
530 March 28 The Arts Centre 5699004
517 March 9 Southern Cross Station 4711595
Month Sensor_ID Sensor_Name Hourly_Counts
3 April 4 Town Hall (West) 12463968
2 April 3 Melbourne Central 10862976
4 April 5 Princes Bridge 10660422
5 April 6 Flinders Street Station Underpass 10135936
0 April 1 Bourke Street Mall (North) 9194871
1 April 2 Bourke Street Mall (South) 8815472
18 April 24 Spencer St-Collins St (North) 6215026
21 April 28 The Arts Centre 6090009
25 April 35 Southbank 6002385
8 April 9 Southern Cross Station 3938379
Month Sensor_ID Sensor_Name Hourly_Counts
589 May 4 Town Hall (West) 11591495
591 May 6 Flinders Street Station Underpass 10787077
588 May 3 Melbourne Central 10615667
586 May 1 Bourke Street Mall (North) 9774547
590 May 5 Princes Bridge 9561337
587 May 2 Bourke Street Mall (South) 8901477
604 May 24 Spencer St-Collins St (North) 6009446
611 May 35 Southbank 5862252
594 May 9 Southern Cross Station 5034469
607 May 28 The Arts Centre 5004039
Month Sensor_ID Sensor_Name Hourly_Counts
435 June 4 Town Hall (West) 11934368
434 June 3 Melbourne Central 10287904
437 June 6 Flinders Street Station Underpass 9867379
432 June 1 Bourke Street Mall (North) 9455301
436 June 5 Princes Bridge 8997795
433 June 2 Bourke Street Mall (South) 8818621
450 June 24 Spencer St-Collins St (North) 6139724
457 June 35 Southbank 5242526
453 June 28 The Arts Centre 5039946
440 June 9 Southern Cross Station 4525398
Month Sensor_ID Sensor_Name Hourly_Counts
370 July 4 Town Hall (West) 12555809
369 July 3 Melbourne Central 10445330
372 July 6 Flinders Street Station Underpass 10203582
367 July 1 Bourke Street Mall (North) 9103779
368 July 2 Bourke Street Mall (South) 9048971
371 July 5 Princes Bridge 8531264
385 July 24 Spencer St-Collins St (North) 6016559
392 July 35 Southbank 4994974
388 July 28 The Arts Centre 4954330
375 July 9 Southern Cross Station 4798905
Month Sensor_ID Sensor_Name Hourly_Counts
80 August 4 Town Hall (West) 11675587
79 August 3 Melbourne Central 10477135
82 August 6 Flinders Street Station Underpass 9214815
77 August 1 Bourke Street Mall (North) 8299742
78 August 2 Bourke Street Mall (South) 8282631
81 August 5 Princes Bridge 7861189
95 August 24 Spencer St-Collins St (North) 5933432
85 August 9 Southern Cross Station 4647033
98 August 28 The Arts Centre 4493078
102 August 35 Southbank 4477946
Month Sensor_ID Sensor_Name Hourly_Counts
799 September 4 Town Hall (West) 11475209
798 September 3 Melbourne Central 9902555
801 September 6 Flinders Street Station Underpass 9602620
797 September 2 Bourke Street Mall (South) 8286561
796 September 1 Bourke Street Mall (North) 8192967
800 September 5 Princes Bridge 7823066
814 September 24 Spencer St-Collins St (North) 6126071
817 September 28 The Arts Centre 4742644
821 September 35 Southbank 4661031
804 September 9 Southern Cross Station 4214830
Month Sensor_ID Sensor_Name Hourly_Counts
733 October 4 Town Hall (West) 11152763
735 October 6 Flinders Street Station Underpass 10333881
730 October 1 Bourke Street Mall (North) 8737215
731 October 2 Bourke Street Mall (South) 8675275
732 October 3 Melbourne Central 8260476
734 October 5 Princes Bridge 7877358
748 October 24 Spencer St-Collins St (North) 6728313
755 October 35 Southbank 5276274
751 October 28 The Arts Centre 5036796
738 October 9 Southern Cross Station 4539958
Month Sensor_ID Sensor_Name Hourly_Counts
667 November 4 Town Hall (West) 12136661
669 November 6 Flinders Street Station Underpass 10598560
665 November 2 Bourke Street Mall (South) 9772438
666 November 3 Melbourne Central 9752026
668 November 5 Princes Bridge 8942629
664 November 1 Bourke Street Mall (North) 8871745
682 November 24 Spencer St-Collins St (North) 6661345
689 November 35 Southbank 5660775
685 November 28 The Arts Centre 5351393
672 November 9 Southern Cross Station 4265450
Month Sensor_ID Sensor_Name Hourly_Counts
145 December 4 Town Hall (West) 14578191
143 December 2 Bourke Street Mall (South) 13134233
147 December 6 Flinders Street Station Underpass 11352132
146 December 5 Princes Bridge 10750919
142 December 1 Bourke Street Mall (North) 10478082
144 December 3 Melbourne Central 9958892
167 December 35 Southbank 7087552
160 December 24 Spencer St-Collins St (North) 5946613
163 December 28 The Arts Centre 5466323
172 December 41 Flinders La-Swanston St (West) 5116437
fig_sc2 = {}
for i in months:
df_month = monthly_count[monthly_count.Month==i].sort_values(by='Hourly_Counts',ascending=False).head(10)
fig_sc2[i]= pexpr.line(df_month, x='Sensor_Name', y= "Hourly_Counts", title=f"Monthly {i} Pedestrian Top 10 location count")
iplot(fig_sc2[i])
lockdown_df = active_sensor_df[active_sensor_df.Date_Time > 'January 1, 2020'].reset_index(drop=True)
lockdown_df_sum_per_month= lockdown_df.groupby(['Sensor_ID','Sensor_Name'],as_index=False).agg({'Hourly_Counts': 'mean'})
lockdown_df_sum_per_month = lockdown_df.groupby(['Sensor_ID','Sensor_Name','Year','Month'],as_index=False).agg({'Hourly_Counts': 'sum'})
lockdown_df_sum_per_month['month_year'] = pd.to_datetime(lockdown_df_sum_per_month.Year.astype(str) + '/' + lockdown_df_sum_per_month.Month.astype(str) + '/01')
most_change = -1
sensor_list = list(lockdown_df_sum_per_month.Sensor_ID.unique())
rate_of_change_sensors = []
for i in sorted(sensor_list):
data_sensor = {}
temp_df = lockdown_df_sum_per_month[lockdown_df_sum_per_month.Sensor_ID == i]
latest_data = temp_df[temp_df.month_year == temp_df.month_year.max()]
oldest_data = temp_df[temp_df.month_year == temp_df.month_year.min()]
if (latest_data.month_year.iloc[0] <= pd.to_datetime('2022-05-01')):
continue
if (oldest_data['month_year'].iloc[0] >= pd.to_datetime('2021-01-01')):
continue
change = (oldest_data['Hourly_Counts'].iloc[0]-latest_data['Hourly_Counts'].iloc[0])/oldest_data['Hourly_Counts'].iloc[0] * 100
data_sensor = {'sensor_id': i, 'Sensor_Name': latest_data['Sensor_Name'].iloc[0], 'oldest_date': oldest_data['month_year'].iloc[0], 'latest_date': latest_data['month_year'].iloc[0], 'oldest_date_count': oldest_data['Hourly_Counts'].iloc[0], 'latest_date_count':latest_data['Hourly_Counts'].iloc[0],'Percentage_changes': change}
rate_of_change_sensors.append(data_sensor)
change_pandemic_df = pd.DataFrame.from_records(rate_of_change_sensors)
top_20 = change_pandemic_df.head(20)
fig_sc3 = pexpr.bar(top_20.sort_values('Percentage_changes', ascending=False), x='Sensor_Name', y= "Percentage_changes", title="Rate of change in Pedestrian count")
top_20
| sensor_id | Sensor_Name | oldest_date | latest_date | oldest_date_count | latest_date_count | Percentage_changes | |
|---|---|---|---|---|---|---|---|
| 0 | 1 | Bourke Street Mall (North) | 2020-01-01 | 2022-06-01 | 891036 | 529691 | 40.553356 |
| 1 | 2 | Bourke Street Mall (South) | 2020-01-01 | 2022-06-01 | 668159 | 406719 | 39.128411 |
| 2 | 3 | Melbourne Central | 2020-01-01 | 2022-06-01 | 758112 | 677908 | 10.579439 |
| 3 | 5 | Princes Bridge | 2020-01-01 | 2022-06-01 | 1246385 | 576741 | 53.726898 |
| 4 | 6 | Flinders Street Station Underpass | 2020-01-01 | 2022-06-01 | 1017087 | 576756 | 43.293347 |
| 5 | 8 | Webb Bridge | 2020-01-01 | 2022-06-01 | 137874 | 106176 | 22.990557 |
| 6 | 9 | Southern Cross Station | 2020-01-01 | 2022-06-01 | 445424 | 245409 | 44.904406 |
| 7 | 10 | Victoria Point | 2020-01-01 | 2022-06-01 | 119372 | 43311 | 63.717622 |
| 8 | 11 | Waterfront City | 2020-01-01 | 2022-06-01 | 43560 | 70674 | -62.245179 |
| 9 | 12 | New Quay | 2020-01-01 | 2022-06-01 | 154753 | 119567 | 22.736877 |
| 10 | 17 | Collins Place (South) | 2020-01-01 | 2022-06-01 | 156265 | 211832 | -35.559466 |
| 11 | 19 | Chinatown-Swanston St (North) | 2020-01-01 | 2022-06-01 | 402209 | 272217 | 32.319515 |
| 12 | 20 | Chinatown-Lt Bourke St (South) | 2020-01-01 | 2022-06-01 | 273963 | 211537 | 22.786289 |
| 13 | 21 | Bourke St-Russell St (West) | 2020-01-01 | 2022-06-01 | 408630 | 325973 | 20.227834 |
| 14 | 23 | Spencer St-Collins St (South) | 2020-01-01 | 2022-06-01 | 273320 | 178031 | 34.863530 |
| 15 | 24 | Spencer St-Collins St (North) | 2020-01-01 | 2022-06-01 | 934105 | 527603 | 43.517806 |
| 16 | 26 | QV Market-Elizabeth St (West) | 2020-01-01 | 2022-06-01 | 381042 | 266345 | 30.100881 |
| 17 | 27 | QV Market-Peel St | 2020-01-01 | 2022-06-01 | 101200 | 87798 | 13.243083 |
| 18 | 28 | The Arts Centre | 2020-01-01 | 2022-06-01 | 890578 | 517525 | 41.888863 |
| 19 | 29 | St Kilda Rd-Alexandra Gardens | 2020-01-01 | 2022-06-01 | 279641 | 169270 | 39.468819 |
fig_sc3
past_year_df = active_sensor_df[active_sensor_df.Date_Time >= pd.to_datetime('2021-06-01')].reset_index(drop=True)
past_year_df_sum_per_month = past_year_df.groupby(['Sensor_ID','Sensor_Name','Year','Month'],as_index=False).agg({'Hourly_Counts': 'sum'})
past_year_df_sum_per_month['month_year'] = pd.to_datetime(past_year_df_sum_per_month.Year.astype(str) + '/' + past_year_df_sum_per_month.Month.astype(str) + '/01')
most_change = -1
sensor_list = list(past_year_df_sum_per_month.Sensor_ID.unique())
rate_of_change_sensors= []
for i in sorted(sensor_list):
data_sensor = {}
temp_df = past_year_df_sum_per_month[past_year_df_sum_per_month.Sensor_ID == i]
latest_data = temp_df[temp_df.month_year == temp_df.month_year.max()]
oldest_data = temp_df[temp_df.month_year == temp_df.month_year.min()]
if (latest_data.month_year.iloc[0] <= pd.to_datetime('2022-05-01')):
continue
change = (latest_data['Hourly_Counts'].iloc[0]-oldest_data['Hourly_Counts'].iloc[0])/latest_data['Hourly_Counts'].iloc[0] * 100
data_sensor = {'sensor_id': i, 'Sensor_Name': latest_data['Sensor_Name'].iloc[0], 'oldest_date': oldest_data['month_year'].iloc[0], 'latest_date': latest_data['month_year'].iloc[0], 'oldest_date_count': oldest_data['Hourly_Counts'].iloc[0], 'latest_date_count':latest_data['Hourly_Counts'].iloc[0],'Percentage_changes': change}
rate_of_change_sensors.append(data_sensor)
change_past_year_df = pd.DataFrame.from_records(rate_of_change_sensors)
top_20_past_year = change_past_year_df.head(20)
fig_sc4 = pexpr.bar(top_20_past_year.sort_values('Percentage_changes', ascending=False), x='Sensor_Name', y= "Percentage_changes", title="Rate of increasing change in Pedestrian count")
top_20_past_year
| sensor_id | Sensor_Name | oldest_date | latest_date | oldest_date_count | latest_date_count | Percentage_changes | |
|---|---|---|---|---|---|---|---|
| 0 | 1 | Bourke Street Mall (North) | 2021-06-01 | 2022-06-01 | 349759 | 529691 | 33.969239 |
| 1 | 2 | Bourke Street Mall (South) | 2021-06-01 | 2022-06-01 | 194777 | 406719 | 52.110179 |
| 2 | 3 | Melbourne Central | 2021-06-01 | 2022-06-01 | 216026 | 677908 | 68.133434 |
| 3 | 5 | Princes Bridge | 2021-06-01 | 2022-06-01 | 358617 | 576741 | 37.820096 |
| 4 | 6 | Flinders Street Station Underpass | 2021-06-01 | 2022-06-01 | 292943 | 576756 | 49.208504 |
| 5 | 8 | Webb Bridge | 2021-06-01 | 2022-06-01 | 81580 | 106176 | 23.165310 |
| 6 | 9 | Southern Cross Station | 2021-06-01 | 2022-06-01 | 61820 | 245409 | 74.809400 |
| 7 | 10 | Victoria Point | 2021-06-01 | 2022-06-01 | 27016 | 43311 | 37.623237 |
| 8 | 11 | Waterfront City | 2021-06-01 | 2022-06-01 | 23332 | 70674 | 66.986445 |
| 9 | 12 | New Quay | 2021-06-01 | 2022-06-01 | 84339 | 119567 | 29.462979 |
| 10 | 17 | Collins Place (South) | 2021-06-01 | 2022-06-01 | 99701 | 211832 | 52.933929 |
| 11 | 19 | Chinatown-Swanston St (North) | 2021-06-01 | 2022-06-01 | 160507 | 272217 | 41.037114 |
| 12 | 20 | Chinatown-Lt Bourke St (South) | 2021-06-01 | 2022-06-01 | 83866 | 211537 | 60.353981 |
| 13 | 21 | Bourke St-Russell St (West) | 2021-06-01 | 2022-06-01 | 172014 | 325973 | 47.230599 |
| 14 | 23 | Spencer St-Collins St (South) | 2021-06-01 | 2022-06-01 | 71717 | 178031 | 59.716566 |
| 15 | 24 | Spencer St-Collins St (North) | 2021-06-01 | 2022-06-01 | 216261 | 527603 | 59.010658 |
| 16 | 26 | QV Market-Elizabeth St (West) | 2021-06-01 | 2022-06-01 | 181100 | 266345 | 32.005482 |
| 17 | 27 | QV Market-Peel St | 2021-06-01 | 2022-06-01 | 53817 | 87798 | 38.703615 |
| 18 | 28 | The Arts Centre | 2021-06-01 | 2022-06-01 | 246249 | 517525 | 52.417951 |
| 19 | 29 | St Kilda Rd-Alexandra Gardens | 2021-06-01 | 2022-06-01 | 51259 | 169270 | 69.717611 |
fig_sc4